1. Technical Field
The present teaching relates to methods, systems, and programming for vertical search. Particularly, the present teaching relates to methods, systems, and programming for predicting search results quality in vertical ranking.
2. Discussion of Technical Background
Vertical search attempts to achieve diversity by presenting search results from different content sources, so-called verticals (e.g., shopping, local, news, finance, etc.), in addition to the standard web results. Vertical search has two main components: (1) pre-retrieval vertical selection, concerned with how to select the verticals from which relevant items can be retrieved, and (2) post-retrieval vertical ranking which ranks the verticals based on the predicted quality of their search results. Such a ranking can be used for selecting which vertical results to present on the search results page and for setting the order of presentation. Post-retrieval vertical ranking, is an important step for vertical search, in particular for projects for which only one or a few vertical results are exposed to the user. The ability to select the most appropriate vertical results for the user has a tremendous effect on the search performance, thus, any improvement in this direction can result in high impact, from business as well as academic points of views.
Existing solutions for post-retrieval vertical ranking are based on learning a ranking function from the user feedback. Any user clicks on a specific vertical presented on the search results page can be interpreted as the user preferring the results of this vertical over the results of preceding presented verticals. These preferences are given as input to a learning-to-rank system which generalizes them to a vertical ranking function. One disadvantage of this approach is that it depends on existing user feedback which might not exist during system launch time. Additionally, the relevance of the vertical results to the query is not fully analyzed by these approaches; only the preferences between vertical results are considered. While the learning, system may consider some features of the vertical results as input to the ranking function, it is not clear whether the user preferences are inferred from the relevance of the vertical search results to the query, or from other reasons such as the presentation bias, from user personal biases, or from other latent reasons.
On the other hand, query performance prediction (QPP) method, such as normalized query commitment (NIX) and weighted information gain (WIG) approaches, is an emerging technology in information retrieval that attempts to predict the relevance of the search results to the user query. Both NQC and WIG use query-based normalization techniques that eliminate query dependent features such as the query length. However, their contribution fir distributed search was found to be marginal because the QPP prediction methods cannot be compared directly across content sources (e.g., verticals, content providers, etc.). Prediction values can be used to compare the quality of search results retrieved by the same vertical for different queries; however, they cannot be used to effectively compare the quality of search results retrieved by different verticals for the same query.
Therefore, there is a need to provide an improved solution for post-retrieval vertical ranking to solve the above-mentioned problems.